lapply - extracting data from nested list in R -

i have download list of addresses google_reverse_code api list of places latitude , longitude information, since i'm new in r. don't know how extract useful information. code downloading databases @ bottom of question.

the structure of list in general this.

`$ 60  :list of 1 ..$ results:'data.frame':   1 obs. of  5 variables: .. ..$ address_components:list of 1 .. .. ..$ :'data.frame':    8 obs. of  3 variables: .. .. .. ..$ long_name : chr [1:8] "119" "avenida diego díaz de berlanga"  "jardines de anahuac 2do sector" "san nicolás de los garza" ... .. .. .. ..$ short_name: chr [1:8] "119" "avenida diego díaz de berlanga"  "jardines de anahuac 2do sector" "san nicolás de los garza" ... .. .. .. ..$ types     :list of 8 .. .. .. .. ..$ : chr "street_number" .. .. .. .. ..$ : chr "route" .. .. .. .. ..$ : chr [1:3] "political" "sublocality" "sublocality_level_1" .. .. .. .. ..$ : chr [1:2] "locality" "political" .. .. .. .. ..$ : chr [1:2] "administrative_area_level_2" "political" .. .. .. .. ..$ : chr [1:2] "administrative_area_level_1" "political" .. .. .. .. ..$ : chr [1:2] "country" "political" .. .. .. .. ..$ : chr "postal_code" .. ..$ formatted_address : chr "avenida diego díaz de berlanga 119, jardines  de anahuac 2do sector, 66444 san nicolás de los garza, n.l., mexico" .. ..$ geometry          :'data.frame': 1 obs. of  3 variables: .. .. ..$ location     :'data.frame':   1 obs. of  2 variables: .. .. .. ..$ lat: num 25.7 .. .. .. ..$ lng: num -100 .. .. ..$ location_type: chr "rooftop" .. .. ..$ viewport     :'data.frame':   1 obs. of  2 variables: .. .. .. ..$ northeast:'data.frame':    1 obs. of  2 variables: .. .. .. .. ..$ lat: num 25.7 .. .. .. .. ..$ lng: num -100 .. .. .. ..$ southwest:'data.frame':    1 obs. of  2 variables: .. .. .. .. ..$ lat: num 25.7 .. .. .. .. ..$ lng: num -100 .. ..$ place_id          : chr "chijry_wpdquyoyrtjett6ajeta" .. ..$ types             :list of 1 .. .. ..$ : chr "street_address" 

i need information data frame perform analysis. information c(latitude, longitude, formatted_address, place_id)

the code have written this:

  prueba <- sapply(direccion1, function(x){ uno <- unlist(x[[1]]) }) 

pureba2 <- data.frame(prueba)

i following error : error in (function (..., row.names = null, check.rows = false, check.names = true, : arguments imply differing number of rows: 40, 32, 37, 44, 36, 0, 41, 28, 39, 47, 43, 35, 48 among other code not work.

the code downloading data contains longitude , latitude following.

 # cre files  library(easypackages)  my_packages <- c("ggmap","maps","mapdata","rlist","readr", "tidyverse",   "lubridate", "stringr", "rebus", "stringi", "purrr", "geosphere", "xml",   "rcurl", "xml2")  libraries(my_packages)   # set link website    link1 <-   ("https://publicacionexterna.azurewebsites.net/publicaciones/prices")   # data webpage   data_prices <- geturl(link1)   # parse xml data   xmlfile <- xmlparse(data_prices)   # place nodes   places <- getnodeset(xmlfile, "//place")   # values each place   values <- lapply(places, function(x){   # current place id   p_id <- xmlattrs(x)   # values each gas type current place   newrows <- lapply(xmlchildren(x), function(y){    # type , update time    attrs <- xmlattrs(y)    # price value    price <- xmlvalue(y)   names(price) <- "price"   # return values   return(c(p_id, attrs, price)        )    })     # combine rows single list     newrows <- do.call(rbind, newrows)     # return rows     return(newrows)      })    # combine values single dataframe    datosdeprecios <- as.data.frame(do.call(rbind, values), stringsasfactors =     false)    # re-set row names dataframe    row.names(datosdeprecios) <- c(1:nrow(datosdeprecios))     # set link website places file     link2 <-     ("https://publicacionexterna.azurewebsites.net/publicaciones/places")      data_places <- read_xml(link2)     datos_id <- data_places %>%      xml_find_all("//place") %>%     xml_attr("place_id")   datos_name <- data_places %>%   xml_find_all("//name") %>%   xml_text("name")   datos_brand <- data_places %>%   xml_find_all("//brand") %>%   xml_text("brand")  datos_cre_id <- data_places %>%   xml_find_all("//cre_id") %>%   xml_text("cre_id")  datos_category <- data_places %>%   xml_find_all("//category") %>%    xml_text("category")   datos_adress_street <- data_places %>%    xml_find_all("//address_street") %>%    xml_text("adress_street")   datos_longitud <- data_places %>%    xml_find_all("//x") %>%    xml_text("x")   datos_latitud <- data_places %>%    xml_find_all("//y") %>%    xml_text("y")   datosdelugares <- data.frame(datos_id, datos_name,                      datos_brand, datos_cre_id,                      datos_category, datos_adress_street,                      datos_latitud, datos_longitud)   colnames(datosdelugares) <- c("place_id", "name", "brand","cre_id",   "category", "adress_street", "latitude", "longitude")    rm(data_prices,places,values,xmlfile,data_places, datos_adress_street,   datos_brand, datos_category, datos_cre_id, datos_id, datos_name,   datos_longitud, datos_latitud)   rm(results, results2) 

the code getting address information following.

datosdeprecios <- datosdeprecios %>%   data.frame(datosdeprecios) %>%   mutate(place_id = as.numeric(place_id))  datosdelugares <- datosdelugares %>%   data.frame(datosdelugares) %>%   mutate(place_id = as.numeric(place_id))   basegeneral <- inner_join(datosdelugares, datosdeprecios, = "place_id")   basegeneral <- basegeneral %>%   select(latitude, longitude, place_id) %>%   mutate(latitude = as.numeric(as.character(latitude))) %>%   mutate(longitude = as.numeric(as.character(longitude)))   basegeneral <- basegeneral[1:100,]  basegeneral <- apply(basegeneral,1 ,function(x) {   google_reverse_geocode(location = c(x["latitude"],x["longitude"]), key =  key, result_type = "street_address") }) 

thank help. :)

you can extract information lists using either [[ notation, or $

if take example given in ?google_reverse_geocode result

library(googleway)  res <- google_reverse_geocode(location = c(-37.81659, 144.9841),                               result_type = c("street_address"),                               location_type = "rooftop",                               key = key) 

the lat/lon information in res$results$geometry$location

the formatted address in res$results$formatted_address

and place_id in res$results$place_id

so can create data.frame these elements

data.frame(   lat = res$results$geometry$location$lat,   lon = res$results$geometry$location$lng,   formatted_address = res$results$formatted_address,   place_id = res$results$place_id ) 

---

if had multiple lists of results, process similar, need wrap in *apply function (or whatever looping mechanism prefer)

## list of locations   locations <- list(c(-37.81659, 144.9841), c(-37.81827, 144.9671))    ## generating reverse geocode each location lst_res <- lapply(locations, function(x){   google_reverse_geocode(location = x, key = key) }) 

here, lst_res list of results geocoding function, can iterate on extract relevant parts

## can extract information  lst_df <- lapply(lst_res, function(x){   data.frame(     lat = x[['results']][['geometry']][['location']][['lat']],     lon = x[['results']][['geometry']][['location']][['lng']],     formatted_address = x[['results']][['formatted_address']],     place_id = x[['results']][['place_id']]   ) }) 

here, lst_df list of data.frames. if want join them 1 single data.frame can

df <- do.call(rbind, lst_df)  ## et voila! head(df) # lat      lon # 1 -37.81647 144.9841 # 2 -37.81659 144.9841 # 3 -37.81300 144.9850 # 4 -37.81363 144.9631 # 5 -37.81614 144.9805 # 6 -37.81005 144.9281 # formatted_address # 1 jolimont station, 175 wellington parade, east melbourne vic 3002, austalia # 2       jolimont station, wellington cres, east melbourne vic 3002, australia # 3                                          east melbourne vic 3002, australia # 4                                                    melbourne vic, australia # 5                                          east melbourne vic 3002, australia # 6                                                   melbourne, vic, australia # place_id # 1 chijsxaubopc1morqhrunmozv4m # 2 chijidtrbupc1mormpt0cxzwbb0 # 3 chijz25svmfc1moraoimixvwbau # 4 chij90260rvg1morkm2mixvwbaq # 5 chijg74w4upd1morsdqurnhwbbw # 6 chijv_fygknd1morpxlurxzurfs